• TA for CPRE288 - Embedded Systems 1 for Spring 2023, Fall 2023, Spring 2024, Fall 2024, and counting.• Facilitated hands-on learning by leading lab sessions and assisting students with questions on embedded C coding, fostering a collaborative and supportive learning environment.• Assessed student assignments objectively, offering constructive feedback to aid their learning process and ensuring fair evaluation of their performance.

• Diagnosed and resolved critical system issues in a custom Yocto Linux build, improving system stability and performance for the Eaton CBC9000 on TI AM62x SOC.• Worked with a cross-functional team using Agile/Scrum, leveraging Jira and Confluence to track progress and resolve challenges efficiently.

• Developed a Python framework to fuzz side channel attack codes, enhancing resistance against machine learning-based detection tools by modifying hardware performance counters (HPCs).• Applied advanced knowledge in microarchitecture and side-channel attacks, such as Spectre, to create effective evasion strategies for cybersecurity threats.• Integrated software engineering and machine learning expertise to disrupt detection mechanisms, demonstrating a deep understanding of both attack methodologies and defense mechanisms.• This research is published in IEEE Journal on Emerging and Selected Topics in Circuits and Systems with the title "Systematical Evasion from Learning-based Microarchitectural Attack Detection Tools".

• Developed firmware for the ESP32 chip using C to establish seamless communication between the Smart Breaker 2.0 and a custom desktop GUI program. • Implemented the Smart Breaker Local Communication Protocol (SBLCP) on the ESP32, ensuring reliable transmission of messages with the breaker.• Developed a custom desktop GUI program using Java to facilitate testing and communication with the Smart Breaker 2.0 using the Smart Breaker Local Communication Protocol (SBLCP).• Contributed to the development of regression tests for the breaker using Python to ensure its functionality and reliability.

• Part of the Boeing Undergraduate Research Excellence in Engineering Program.• Generate high-resolution mapping to improve snow-plow navigation under whiteout conditions using Raspberry Pi running Robot Operating System (ROS) on Ubuntu.• Used ROS to process data collected by sensors like GPS, IMU, and Radar.

• Provide high-quality customer service by informing them of up-to-date device (laptops, accessories, etc.) recommendations and assisting customers with decision-making.• Collaborated with other sales associates to maintain and organize sales floor.